Part one
D = ( 1 1 2 2 1 1 1 1 3 ) D= \begin{pmatrix}
1&1 &2 \\
2&1 & 1\\
1&1&3
\end{pmatrix} D = ⎝ ⎛ 1 2 1 1 1 1 2 1 3 ⎠ ⎞
Characteristic equation d e t ∣ ∣ D − λ I ∣ ∣ = 0 det||D-\lambda\Iota||=0 d e t ∣∣ D − λ I ∣∣ = 0
D e t ( 1 − λ 1 2 2 1 − λ 1 1 1 3 − λ ) Det \begin{pmatrix}
1-\lambda&1&2 \\
2&1-\lambda&1\\
1&1&3-\lambda
\end{pmatrix} De t ⎝ ⎛ 1 − λ 2 1 1 1 − λ 1 2 1 3 − λ ⎠ ⎞
Characteristic polynomial is of the form;
λ 3 − A 1 λ 2 + A 2 λ − A 3 = 0 \lambda^3-A_1\lambda^2+A_2\lambda-A_3=0 λ 3 − A 1 λ 2 + A 2 λ − A 3 = 0
Where A 1 = A_1= A 1 = sum of main diagonal element
= 1 + 1 + 3 = 5 =1+1+3=5 = 1 + 1 + 3 = 5
A 2 = A_2= A 2 = sum of minors of the main diagonal element
A 2 = ∣ 1 1 1 3 ∣ + ∣ 1 2 1 3 ∣ + ∣ 1 1 2 1 ∣ = 2 + 1 − 1 = 2 A_2= \begin{vmatrix}
1 & 1 \\
1 & 3
\end{vmatrix}+\begin{vmatrix}
1 & 2 \\
1 & 3
\end{vmatrix}+\begin{vmatrix}
1& 1 \\
2 & 1
\end{vmatrix}=2+1-1=2 A 2 = ∣ ∣ 1 1 1 3 ∣ ∣ + ∣ ∣ 1 1 2 3 ∣ ∣ + ∣ ∣ 1 2 1 1 ∣ ∣ = 2 + 1 − 1 = 2
A 3 = d e t ( D ) = 1 ∣ 1 1 1 3 ∣ − 1 ∣ 2 1 1 3 ∣ + 2 ∣ 2 1 1 1 ∣ A_3= det(D)=\quad\quad\quad\quad1\begin{vmatrix}
1 & 1\\
1 & 3
\end{vmatrix}-1\begin{vmatrix}
2& 1\\
1 & 3
\end{vmatrix}+2\begin{vmatrix}
2& 1\\
1& 1
\end{vmatrix} A 3 = d e t ( D ) = 1 ∣ ∣ 1 1 1 3 ∣ ∣ − 1 ∣ ∣ 2 1 1 3 ∣ ∣ + 2 ∣ ∣ 2 1 1 1 ∣ ∣
= 1 ( 2 ) − 1 ( 5 ) + 2 ( 1 ) =1(2)-1(5)+2(1) = 1 ( 2 ) − 1 ( 5 ) + 2 ( 1 )
= − 1 =-1 = − 1
Characteristic polynomial is
λ 3 − 5 λ 2 + 2 λ + 1 = 0 \lambda^3-5\lambda^2+2\lambda+1=0 λ 3 − 5 λ 2 + 2 λ + 1 = 0
Let λ 3 − 5 λ 2 + 2 λ + 1 = 0 \lambda^3-5\lambda^2+2\lambda+1=0 λ 3 − 5 λ 2 + 2 λ + 1 = 0
f ( λ ) = λ 3 − 5 λ 2 + 2 λ + 1 f(\lambda)=\lambda^3-5\lambda^2+2\lambda+1 f ( λ ) = λ 3 − 5 λ 2 + 2 λ + 1
1st iteration
λ 0 = 5 \lambda_0=5 λ 0 = 5 and λ 1 = 4 \lambda_1=4 λ 1 = 4
f ( λ 0 ) = f ( s ) = 11 f(\lambda_0)=f(s)=11 f ( λ 0 ) = f ( s ) = 11
f ( λ 1 ) = f ( 4 ) = − 7 f(\lambda_1)=f(4)=-7 f ( λ 1 ) = f ( 4 ) = − 7
∴ λ 2 = 5 − 11 \therefore\>\lambda_2=5-11 ∴ λ 2 = 5 − 11 4 − 5 − 7 − 11 = 4.3889 \frac{4-5}{-7-11}=4.3889 − 7 − 11 4 − 5 = 4.3889
f ( λ 2 ) = − 1.9937 f(\lambda_2)=-1.9937 f ( λ 2 ) = − 1.9937
2nd iteration
λ 1 = 4 \lambda_1=4 λ 1 = 4 and λ 2 = 4.3889 \lambda_2=4.3889 λ 2 = 4.3889
f ( λ 1 ) = f ( 4 ) = − 7 f(\lambda_1)=f(4)=-7 f ( λ 1 ) = f ( 4 ) = − 7
f ( λ 2 ) = f ( 4.3889 ) = − 1.9937 f(\lambda_2)=f(4.3889)= -1.9937 f ( λ 2 ) = f ( 4.3889 ) = − 1.9937
λ 3 = 4 − ( − 7 ) \lambda_3=4-(-7) λ 3 = 4 − ( − 7 ) 4.3889 − 4 − 1.9937 − ( − 7 ) = 4.5438 \frac{4.3889-4}{-1.9937-(-7)}=4.5438 − 1.9937 − ( − 7 ) 4.3889 − 4 = 4.5438
3rd iteration
λ 2 = 4.3889 \lambda_2=4.3889 λ 2 = 4.3889 and λ 3 = 4.5438 \lambda_3=4.5438 λ 3 = 4.5438
f ( λ 2 ) = − 1.9937 f(\lambda_2)=-1.9937 f ( λ 2 ) = − 1.9937
f ( λ 3 ) = f ( 4.5438 ) = 0.6688 f(\lambda_3)=f(4.5438)=0.6688 f ( λ 3 ) = f ( 4.5438 ) = 0.6688
λ 4 = 4.3889 − ( − 1.9937 ) \lambda_4=4.3889-(-1.9937) λ 4 = 4.3889 − ( − 1.9937 )
4.5438 − 4.3889 0.6688 − − 1.9937 \frac{4.5438-4.3889}{0.6688-\>-1.9937} 0.6688 − − 1.9937 4.5438 − 4.3889
= 4.5049 =4.5049 = 4.5049
f ( λ 4 ) = f ( 4.5049 ) = − 0.0378 f(\lambda_4)=f(4.5049)=-0.0378 f ( λ 4 ) = f ( 4.5049 ) = − 0.0378
4th iteration
λ 3 = 4.5438 \lambda_3=4.5438 λ 3 = 4.5438 and λ 4 = 4.5049 \lambda_4=4.5049 λ 4 = 4.5049
f ( λ 3 ) = 0.6688 f(\lambda_3)=0.6688 f ( λ 3 ) = 0.6688
f ( λ 4 ) = − 0.0378 f(\lambda_4)=-0.0378 f ( λ 4 ) = − 0.0378
λ 5 = 4.5438 − 0.6688 \lambda_5=4.5438-0.6688 λ 5 = 4.5438 − 0.6688
4.5049 − 4.5438 − 0.0378 − 0.6688 \frac{4.5049-4.5438}{-0.0378-0.6688} − 0.0378 − 0.6688 4.5049 − 4.5438
= 4.5070 =4.5070 = 4.5070
f ( λ 5 ) = − 0.0003 f(\lambda_5)=-0.0003 f ( λ 5 ) = − 0.0003
Approximate root = 4.507 =4.507 = 4.507
Part two
Q Q Q -R R R method
Let a 1 = [ 1 2 1 ] a_1= \begin{bmatrix}
1&2&1
\end{bmatrix} a 1 = [ 1 2 1 ] a 2 = [ 1 1 1 ] a_2=\begin{bmatrix}
1&1& 1
\end{bmatrix} a 2 = [ 1 1 1 ]
and
a 3 = [ 2 1 3 ] a_3=\begin{bmatrix}
2&1 & 3
\end{bmatrix} a 3 = [ 2 1 3 ]
Let orthogonal set = ( b 1 b 2 b 3 ) =\begin{pmatrix}
b_1& b_2&b_3
\end{pmatrix} = ( b 1 b 2 b 3 )
Let orthonormal set = ( q 1 q 2 q 3 ) =\begin{pmatrix}
q_1&q_2& q_3
\end{pmatrix} = ( q 1 q 2 q 3 )
Let b 1 = [ 1 2 1 ] , b_1= \begin{bmatrix}
1&2 & 1
\end{bmatrix}, b 1 = [ 1 2 1 ] ,
∣ ∣ b 1 ∣ ∣ = 1 2 + 2 2 + 1 2 = 2.45 ||b_1||=\sqrt{1^2+2^2+1^2}=2.45 ∣∣ b 1 ∣∣ = 1 2 + 2 2 + 1 2 = 2.45
q 1 = 1 2.45 [ 1 2 1 ] = [ 0.41 , 0.82 , 0.41 ] q_1=\frac{1}{2.45}\begin{bmatrix}
1&2 & 1
\end{bmatrix}=\begin{bmatrix}
0.41,&0.82,& 0.41
\end{bmatrix} q 1 = 2.45 1 [ 1 2 1 ] = [ 0.41 , 0.82 , 0.41 ]
b 2 = [ 1 1 1 ] − b_2=\begin{bmatrix}
1&1& 1
\end{bmatrix}- b 2 = [ 1 1 1 ] −
< ( 1 , 1 , 1 ) , ( 0.41 , 0.82 , 0.41 ) > ( 0.41 , 0.82 , 0.41 ) <(1,1,1),(0.41,0.82,0.41)>(0.41,0.82,0.41) < ( 1 , 1 , 1 ) , ( 0.41 , 0.82 , 0.41 ) > ( 0.41 , 0.82 , 0.41 )
= ( 0.33 , − 0.33 , 0.33 ) =\begin{pmatrix}
0.33,&-0.33,&0.33
\end{pmatrix} = ( 0.33 , − 0.33 , 0.33 )
∣ ∣ b 2 ∣ ∣ = 0.3 3 2 + 0.3 3 2 + 0.3 3 2 = ||b_2||= \sqrt{ 0.33^2+0.33^2+0.33^2}= ∣∣ b 2 ∣∣ = 0.3 3 2 + 0.3 3 2 + 0.3 3 2 =
0.58 0.58 0.58
q 2 = 1 0.58 [ 0.33 , − 0.33 0.33 ] = q_2= \frac{1}{0.58}\begin{bmatrix}
0.33,&-0.33& 0.33
\end{bmatrix}= q 2 = 0.58 1 [ 0.33 , − 0.33 0.33 ] =
( 0.58 , − 0.58 , 0.58 ) \begin{pmatrix}
0.58,&-0.58,&0.58
\end{pmatrix} ( 0.58 , − 0.58 , 0.58 )
b 3 = ( 2 , 1 , 3 ) b_3=\begin{pmatrix}
2,&1,&3
\end{pmatrix} b 3 = ( 2 , 1 , 3 ) − - −
− < ( 2 , 1 , 3 ) , ( 0.41 , 0.82 , 0.41 ) > -<(2,1,3),(0.41,0.82,0.41)> − < ( 2 , 1 , 3 ) , ( 0.41 , 0.82 , 0.41 ) >
( 0.41 , 0.82 , 0.41 ) (0.41,0.82,0.41) ( 0.41 , 0.82 , 0.41 )
− < ( 2 , 1 , 3 ) , ( 0.58 , − 0.58 , 0.58 ) > - <(2,1,3),(0.58,-0.58,0.58)> − < ( 2 , 1 , 3 ) , ( 0.58 , − 0.58 , 0.58 ) >
b 3 = ( − 0.5 , 0 , 0.5 ) ∣ ∣ b 3 ∣ ∣ = b_3=(-0.5,0,0.5)\quad ||b_3||= b 3 = ( − 0.5 , 0 , 0.5 ) ∣∣ b 3 ∣∣ =
0. 5 2 + 0 2 + 0. 5 2 = 0.71 \sqrt{0.5^2+0^2+0.5^2}=0.71 0. 5 2 + 0 2 + 0. 5 2 = 0.71
q 3 = 1 0.71 ( − 0.5 , 0 , 0.5 ) = q_3=\frac{1}{0.71}(-0.5,0,0.5)= q 3 = 0.71 1 ( − 0.5 , 0 , 0.5 ) =
( − 0.71 , 0 , 0.71 ) (-0.71,0,0.71) ( − 0.71 , 0 , 0.71 )
∴ Q = [ q 1 q 2 q 3 ] = \therefore Q= \begin{bmatrix}
q_1&q_2& q_3
\end{bmatrix}= ∴ Q = [ q 1 q 2 q 3 ] =
[ 0.41 0.58 − 0.71 0.82 − 0.58 0 0.41 0.58 0.71 ] \begin{bmatrix}
0.41&0.58 & -0.71\\
0.82&-0.58 & 0\\
0.41&0.58&0.71
\end{bmatrix} ⎣ ⎡ 0.41 0.82 0.41 0.58 − 0.58 0.58 − 0.71 0 0.71 ⎦ ⎤
R = Q T A = ( 0.41 0.82 0.41 0.58 − 0.58 0.58 − 0.71 0 0.71 ) ( 1 1 2 2 1 1 1 1 3 ) R=Q^TA=\begin{pmatrix}
0.41&0.82 & 0.41\\
0.58&-0.58 & 0.58\\
-0.71&0&0.71
\end{pmatrix}\begin{pmatrix}
1&1 & 2\\
2&1& 1\\
1&1&3
\end{pmatrix} R = Q T A = ⎝ ⎛ 0.41 0.58 − 0.71 0.82 − 0.58 0 0.41 0.58 0.71 ⎠ ⎞ ⎝ ⎛ 1 2 1 1 1 1 2 1 3 ⎠ ⎞
= ( 2.45 1.63 2.86 0 0.58 2.31 0 0 0.71 ) =\begin{pmatrix}
2.45&1.63& 2.86\\
0&0.58&2.31\\
0&0&0.71
\end{pmatrix} = ⎝ ⎛ 2.45 0 0 1.63 0.58 0 2.86 2.31 0.71 ⎠ ⎞
Denoting the above R R R as R 0 R_0 R 0 and the Q Q Q as Q 0 Q_0 Q 0
Let A 1 = R 0 Q 0 A_1=R_0Q_0 A 1 = R 0 Q 0
Let A 1 = Q 1 R 1 A_1=Q_1R_1 A 1 = Q 1 R 1 be Q R QR QR factorization of A 1 A_1 A 1 and similarly create A 2 = R 1 Q 1 A_2=R_1Q_1 A 2 = R 1 Q 1
This process is continued until A m A_m A m is created such that A m = Q m R m A_m=Q_mR_m A m = Q m R m and A m 1 = R m Q m A_{m1}=R_mQ_m A m 1 = R m Q m are equal.
At convergence, the diagonal entries below the main diagonal are sufficiently small.
The diagonal entries of A m A_m A m will be eigenvalues of matrix A A A
The expected values will be approximate to 0.28514 , 0.77812 a n d 4.50701 0.28514,0.77812 \>and \> 4 .50701 0.28514 , 0.77812 an d 4.50701
Comments